204 lines
7.7 KiB
JavaScript
204 lines
7.7 KiB
JavaScript
import * as math from 'mathjs'
|
|
import { ANON_USER_ID, SN_USER_IDS } from '../lib/constants.js'
|
|
|
|
export async function trust ({ boss, models }) {
|
|
try {
|
|
console.time('trust')
|
|
console.timeLog('trust', 'getting graph')
|
|
const graph = await getGraph(models)
|
|
console.timeLog('trust', 'computing trust')
|
|
const [vGlobal, mPersonal] = await trustGivenGraph(graph)
|
|
console.timeLog('trust', 'storing trust')
|
|
await storeTrust(models, graph, vGlobal, mPersonal)
|
|
} finally {
|
|
console.timeEnd('trust')
|
|
}
|
|
}
|
|
|
|
const MAX_DEPTH = 10
|
|
const MAX_TRUST = 1
|
|
const MIN_SUCCESS = 1
|
|
// increasing disgree_mult increases distrust when there's disagreement
|
|
// ... this cancels DISAGREE_MULT number of "successes" for every disagreement
|
|
const DISAGREE_MULT = 10
|
|
// https://en.wikipedia.org/wiki/Normal_distribution#Quantile_function
|
|
const Z_CONFIDENCE = 6.109410204869 // 99.9999999% confidence
|
|
const GLOBAL_ROOT = 616
|
|
const SEED_WEIGHT = 0.25
|
|
const AGAINST_MSAT_MIN = 1000
|
|
const MSAT_MIN = 1000
|
|
const SIG_DIFF = 0.1 // need to differ by at least 10 percent
|
|
|
|
/*
|
|
Given a graph and start this function returns an object where
|
|
the keys are the node id and their value is the trust of that node
|
|
*/
|
|
function trustGivenGraph (graph) {
|
|
// empty matrix of proper size nstackers x nstackers
|
|
let mat = math.zeros(graph.length, graph.length, 'sparse')
|
|
|
|
// create a map of user id to position in matrix
|
|
const posByUserId = {}
|
|
for (const [idx, val] of graph.entries()) {
|
|
posByUserId[val.id] = idx
|
|
}
|
|
|
|
// iterate over graph, inserting edges into matrix
|
|
for (const [idx, val] of graph.entries()) {
|
|
for (const { node, trust } of val.hops) {
|
|
try {
|
|
mat.set([idx, posByUserId[node]], Number(trust))
|
|
} catch (e) {
|
|
console.log('error:', idx, node, posByUserId[node], trust)
|
|
throw e
|
|
}
|
|
}
|
|
}
|
|
|
|
// perform random walk over trust matrix
|
|
// the resulting matrix columns represent the trust a user (col) has for each other user (rows)
|
|
// XXX this scales N^3 and mathjs is slow
|
|
let matT = math.transpose(mat)
|
|
const original = matT.clone()
|
|
for (let i = 0; i < MAX_DEPTH; i++) {
|
|
console.timeLog('trust', `matrix multiply ${i}`)
|
|
matT = math.multiply(original, matT)
|
|
matT = math.add(math.multiply(1 - SEED_WEIGHT, matT), math.multiply(SEED_WEIGHT, original))
|
|
}
|
|
|
|
console.timeLog('trust', 'transforming result')
|
|
|
|
const seedIdxs = SN_USER_IDS.map(id => posByUserId[id])
|
|
const isOutlier = (fromIdx, idx) => [...seedIdxs, fromIdx].includes(idx)
|
|
const sqapply = (mat, fn) => {
|
|
let idx = 0
|
|
return math.squeeze(math.apply(mat, 1, d => {
|
|
const filtered = math.filter(d, (val, fidx) => {
|
|
return val !== 0 && !isOutlier(idx, fidx[0])
|
|
})
|
|
idx++
|
|
if (filtered.length === 0) return 0
|
|
return fn(filtered)
|
|
}))
|
|
}
|
|
|
|
console.timeLog('trust', 'normalizing')
|
|
console.timeLog('trust', 'stats')
|
|
mat = math.transpose(matT)
|
|
const std = sqapply(mat, math.std) // math.squeeze(math.std(mat, 1))
|
|
const mean = sqapply(mat, math.mean) // math.squeeze(math.mean(mat, 1))
|
|
const zscore = math.map(mat, (val, idx) => {
|
|
const zstd = math.subset(std, math.index(idx[0]))
|
|
const zmean = math.subset(mean, math.index(idx[0]))
|
|
return zstd ? (val - zmean) / zstd : 0
|
|
})
|
|
console.timeLog('trust', 'minmax')
|
|
const min = sqapply(zscore, math.min) // math.squeeze(math.min(zscore, 1))
|
|
const max = sqapply(zscore, math.max) // math.squeeze(math.max(zscore, 1))
|
|
const mPersonal = math.map(zscore, (val, idx) => {
|
|
const zmin = math.subset(min, math.index(idx[0]))
|
|
const zmax = math.subset(max, math.index(idx[0]))
|
|
const zrange = zmax - zmin
|
|
if (val > zmax) return MAX_TRUST
|
|
return zrange ? (val - zmin) / zrange : 0
|
|
})
|
|
const vGlobal = math.squeeze(math.row(mPersonal, posByUserId[GLOBAL_ROOT]))
|
|
|
|
return [vGlobal, mPersonal]
|
|
}
|
|
|
|
/*
|
|
graph is returned as json in adjacency list where edges are the trust value 0-1
|
|
graph = [
|
|
{ id: node1, hops: [{node : node2, trust: trust12}, {node: node3, trust: trust13}] },
|
|
...
|
|
]
|
|
*/
|
|
async function getGraph (models) {
|
|
return await models.$queryRaw`
|
|
SELECT id, json_agg(json_build_object(
|
|
'node', oid,
|
|
'trust', CASE WHEN total_trust > 0 THEN trust / total_trust::float ELSE 0 END)) AS hops
|
|
FROM (
|
|
WITH user_votes AS (
|
|
SELECT "ItemAct"."userId" AS user_id, users.name AS name, "ItemAct"."itemId" AS item_id, min("ItemAct".created_at) AS act_at,
|
|
users.created_at AS user_at, "ItemAct".act = 'DONT_LIKE_THIS' AS against,
|
|
count(*) OVER (partition by "ItemAct"."userId") AS user_vote_count
|
|
FROM "ItemAct"
|
|
JOIN "Item" ON "Item".id = "ItemAct"."itemId" AND "ItemAct".act IN ('FEE', 'TIP', 'DONT_LIKE_THIS')
|
|
AND "Item"."parentId" IS NULL AND NOT "Item".bio AND "Item"."userId" <> "ItemAct"."userId"
|
|
JOIN users ON "ItemAct"."userId" = users.id AND users.id <> ${ANON_USER_ID}
|
|
GROUP BY user_id, name, item_id, user_at, against
|
|
HAVING CASE WHEN
|
|
"ItemAct".act = 'DONT_LIKE_THIS' THEN sum("ItemAct".msats) > ${AGAINST_MSAT_MIN}
|
|
ELSE sum("ItemAct".msats) > ${MSAT_MIN} END
|
|
),
|
|
user_pair AS (
|
|
SELECT a.user_id AS a_id, b.user_id AS b_id,
|
|
count(*) FILTER(WHERE a.act_at > b.act_at AND a.against = b.against) AS before,
|
|
count(*) FILTER(WHERE b.act_at > a.act_at AND a.against = b.against) AS after,
|
|
count(*) FILTER(WHERE a.against <> b.against) * ${DISAGREE_MULT} AS disagree,
|
|
b.user_vote_count AS b_total, a.user_vote_count AS a_total
|
|
FROM user_votes a
|
|
JOIN user_votes b ON a.item_id = b.item_id
|
|
WHERE a.user_id <> b.user_id
|
|
GROUP BY a.user_id, a.user_vote_count, b.user_id, b.user_vote_count
|
|
),
|
|
trust_pairs AS (
|
|
SELECT a_id AS id, b_id AS oid,
|
|
CASE WHEN before - disagree >= ${MIN_SUCCESS} AND b_total - after > 0 THEN
|
|
confidence(before - disagree, b_total - after, ${Z_CONFIDENCE})
|
|
ELSE 0 END AS trust
|
|
FROM user_pair
|
|
WHERE b_id <> ANY (${SN_USER_IDS})
|
|
UNION ALL
|
|
SELECT a_id AS id, seed_id AS oid, ${MAX_TRUST}::numeric as trust
|
|
FROM user_pair, unnest(${SN_USER_IDS}::int[]) seed_id
|
|
GROUP BY a_id, a_total, seed_id
|
|
UNION ALL
|
|
SELECT a_id AS id, a_id AS oid, ${MAX_TRUST}::float as trust
|
|
FROM user_pair
|
|
)
|
|
SELECT id, oid, trust, sum(trust) OVER (PARTITION BY id) AS total_trust
|
|
FROM trust_pairs
|
|
) a
|
|
GROUP BY a.id
|
|
ORDER BY id ASC`
|
|
}
|
|
|
|
async function storeTrust (models, graph, vGlobal, mPersonal) {
|
|
// convert nodeTrust into table literal string
|
|
let globalValues = ''
|
|
let personalValues = ''
|
|
vGlobal.forEach((val, [idx]) => {
|
|
if (isNaN(val)) return
|
|
if (globalValues) globalValues += ','
|
|
globalValues += `(${graph[idx].id}, ${val}::FLOAT)`
|
|
if (personalValues) personalValues += ','
|
|
personalValues += `(${GLOBAL_ROOT}, ${graph[idx].id}, ${val}::FLOAT)`
|
|
})
|
|
|
|
math.forEach(mPersonal, (val, [fromIdx, toIdx]) => {
|
|
const globalVal = vGlobal.get([toIdx])
|
|
if (isNaN(val) || val - globalVal <= SIG_DIFF) return
|
|
if (personalValues) personalValues += ','
|
|
personalValues += `(${graph[fromIdx].id}, ${graph[toIdx].id}, ${val}::FLOAT)`
|
|
})
|
|
|
|
// update the trust of each user in graph
|
|
await models.$transaction([
|
|
models.$executeRaw`UPDATE users SET trust = 0`,
|
|
models.$executeRawUnsafe(
|
|
`UPDATE users
|
|
SET trust = g.trust
|
|
FROM (values ${globalValues}) g(id, trust)
|
|
WHERE users.id = g.id`),
|
|
models.$executeRawUnsafe(
|
|
`INSERT INTO "Arc" ("fromId", "toId", "zapTrust")
|
|
SELECT id, oid, trust
|
|
FROM (values ${personalValues}) g(id, oid, trust)
|
|
ON CONFLICT ("fromId", "toId") DO UPDATE SET "zapTrust" = EXCLUDED."zapTrust"`
|
|
)
|
|
])
|
|
}
|